Role Overview
Generative AI Engineer (35 Years Experience)
We are seeking a Generative AI Engineer to design and implement AI-driven solutions that automate document processing, decision support, and analytical workflows. The role will focus on building
LLM-powered applications using Retrieval-Augmented Generation (RAG) to deliver accurate, explainable, and domain-aware outputs from large, complex datasets.
Key Responsibilities
- Design, build, and deploy RAG-based AI solutions using large language models for document analysis, summarization, and intelligent automation.
- Develop and optimize retrieval pipelines leveraging embeddings and vector databases.
- Implement prompt engineering strategies and context-aware generation workflows.
- Integrate LLM applications into production systems via APIs and microservices.
- Collaborate with data engineering teams to prepare structured and unstructured data for retrieval and indexing.
- Evaluate model outputs for accuracy, relevance, and hallucination reduction.
- Monitor AI pipelines and continuously improve system performance.
- Ensure responsible AI usage, data privacy, and compliance with regulatory requirements.
Required Skills & Experience
- 35 years of experience in AI/ML, NLP, or applied machine learning roles.
- Strong proficiency in Python and ML frameworks (PyTorch, TensorFlow, Hugging Face).
- Hands-on experience with LLMs and RAG architectures.
- Experience with vector databases (e.g., FAISS, Pinecone, Weaviate, Chroma).
- Knowledge of embeddings, similarity search, and retrieval strategies.
- Experience deploying AI models and services in cloud environments.
- Solid understanding of data security and governance principles.
Nice to Have
- Experience working with healthcare, insurance, legal, or regulatory text data.
- Familiarity with document ingestion pipelines (PDFs, scanned docs, OCR).
- Exposure to model monitoring, evaluation frameworks, and guardrails.
Skills: kubernetes,rag,gcp,azure,aws,llm,pytorch,faiss,docker,pipelines
